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        <div class="note success"><p>网易云课堂-吴恩达-机器学习10天学习计划</p></div>
<h3 id="章节1-绪论：初识机器学习"><a href="#章节1-绪论：初识机器学习" class="headerlink" title="章节1 绪论：初识机器学习"></a>章节1 绪论：初识机器学习</h3><p>什么是机器学习，监督学习，无监督学习</p>
<h3 id="章节2-单变量线性回归"><a href="#章节2-单变量线性回归" class="headerlink" title="章节2 单变量线性回归"></a>章节2 单变量线性回归</h3><a id="more"></a>
<p>模型描述，代价函数，梯度下降</p>
<h3 id="章节3-线性回归回顾"><a href="#章节3-线性回归回顾" class="headerlink" title="章节3 线性回归回顾"></a>章节3 线性回归回顾</h3><p>矩阵运算，线性代数基础知识</p>
<h3 id="章节4-配置"><a href="#章节4-配置" class="headerlink" title="章节4 配置"></a>章节4 配置</h3><p>安装MATLAB，Octave（跨平台数学计算库）</p>
<h3 id="章节5-多变量线性回归"><a href="#章节5-多变量线性回归" class="headerlink" title="章节5 多变量线性回归"></a>章节5 多变量线性回归</h3><p>多元梯度下降：1特征缩放 2学习率α，特征和多项式回归<br>正规方程直接求解 pinv()伪逆</p>
<h3 id="章节6-Octave-Matlab-教程"><a href="#章节6-Octave-Matlab-教程" class="headerlink" title="章节6 Octave/Matlab 教程"></a>章节6 Octave/Matlab 教程</h3><p>Octave基础语法，与matlab很像</p>
<h3 id="章节7-Logistic回归"><a href="#章节7-Logistic回归" class="headerlink" title="章节7 Logistic回归"></a>章节7 Logistic回归</h3><p>Logistic回归是分类问题，假设陈述，决策界限，代价函数。<br>高级优化（Conjugate gradient，BFGS，L-BFGS）多元分类</p>
<h3 id="章节8-正则化"><a href="#章节8-正则化" class="headerlink" title="章节8 正则化"></a>章节8 正则化</h3><p>用来解决过拟合问题，泛化</p>
<h3 id="章节9-神经网络学习"><a href="#章节9-神经网络学习" class="headerlink" title="章节9 神经网络学习"></a>章节9 神经网络学习</h3><p>非线性假设，非线性分类，多元分类</p>
<h3 id="章节10-神经网络学参数的反向传播算法"><a href="#章节10-神经网络学参数的反向传播算法" class="headerlink" title="章节10 神经网络学参数的反向传播算法"></a>章节10 神经网络学参数的反向传播算法</h3><p>训练神经网络的6个步骤</p>
<ol>
<li>构建网络并随机初始化权重</li>
<li>前向传播</li>
<li>计算代价函数</li>
<li>反向传播求导</li>
<li>梯度检查，然后停用掉（保证用BP计算的导数是正确的即可） </li>
<li>使用梯度下降或更高级的算法来最小化代价函数</li>
</ol>
<p>神经网络的应用，自动驾驶，无人驾驶666</p>
<h3 id="章节11-应用机器学习的建议"><a href="#章节11-应用机器学习的建议" class="headerlink" title="章节11 应用机器学习的建议"></a>章节11 应用机器学习的建议</h3><p>评估假设，6 2 2分为训练集 验证集 测试集<br>诊断偏差（欠拟合）与方差（过拟合）<br>正则化解决：对于高偏差减小λ（因为λ大代表惩罚严重），对于高方差增大λ（因为λ小代表惩罚太清）<br>学习曲线的描绘很重要，评估是否增大样本，对于高偏差增大样本无效，对于高方差可以增大样本</p>
<h3 id="章节12-机器学习系统设计"><a href="#章节12-机器学习系统设计" class="headerlink" title="章节12 机器学习系统设计"></a>章节12 机器学习系统设计</h3><p>误差分析，精确度和召回率的权衡</p>
<h3 id="章节13-支持向量机"><a href="#章节13-支持向量机" class="headerlink" title="章节13 支持向量机"></a>章节13 支持向量机</h3><p>大间隔分类器，核函数，线性核函数，高斯核函数<br>学这章时注意力不集中</p>
<h3 id="章节14-无监督学习"><a href="#章节14-无监督学习" class="headerlink" title="章节14 无监督学习"></a>章节14 无监督学习</h3><p>聚类算法，K-Means，随机初始化聚类中心，步骤如下：</p>
<ol>
<li>簇分配</li>
<li>移动聚类中心</li>
</ol>
<h3 id="章节15-降维"><a href="#章节15-降维" class="headerlink" title="章节15 降维"></a>章节15 降维</h3><p>降维目标：</p>
<ol>
<li>数据压缩</li>
<li>可视化</li>
</ol>
<p>PCA主成分分析，课时119 120没有字幕<br>利用PCA来加速监督学习，不过为了防止过拟合，不推荐用降维，要用正则化。<br>设计机器学习系统时先不用PCA，如果效果不好再试试加入PCA。</p>
<h3 id="章节16-异常检测"><a href="#章节16-异常检测" class="headerlink" title="章节16 异常检测"></a>章节16 异常检测</h3><p>欺诈检测，用户行为，数据中心的计算机负载检测<br>高斯分布（正态分布），钟形曲线<br>正样本数少时，异常检测。正样本数多时，监督学习。<br>异常检测算法的特征选取：选取不太大也不太小的值，并可通过已有特征的组合构造出新特征。<br>多元高斯分布（多变量高斯分布），协方差矩阵，要求m&gt;&gt;n，即样本数量远大于特征数量，否则会造成协方差矩阵不可逆。</p>
<h3 id="章节17-推荐系统"><a href="#章节17-推荐系统" class="headerlink" title="章节17 推荐系统"></a>章节17 推荐系统</h3><p>基于内容的推荐算法（电影特征）<br>协同过滤，能够自动学习所需要的特征，低秩矩阵分解，均值归一化。</p>
<h3 id="章节18-大规模机器学习"><a href="#章节18-大规模机器学习" class="headerlink" title="章节18 大规模机器学习"></a>章节18 大规模机器学习</h3><p>随机梯度下降：</p>
<ol>
<li>随机打乱所有数据</li>
<li>遍历一次，随机迂回</li>
</ol>
<p>Mini-Bathch梯度下降有时更快，是随机梯度下降（一次一个样本求偏导）和批量梯度下降（一次m个样本求偏导）的折中。<br>如果代价函数的曲线振荡，可以尝试增大求均值的样本数量，如果代价函数的曲线不降反增，则尝试减小α（学习速率）。<br>在线学习，点击率预测学习问题，与随机梯度算法非常类似。<br>Map-reduce，并行计算</p>
<h3 id="章节19-应用举例：照片OCR（照片光学字符识别）"><a href="#章节19-应用举例：照片OCR（照片光学字符识别）" class="headerlink" title="章节19 应用举例：照片OCR（照片光学字符识别）"></a>章节19 应用举例：照片OCR（照片光学字符识别）</h3><p>机器学习流水线技术pipeline<br>低偏差机器学习算法，人工数据合成，众包（Amazon Mechanical Turk）<br>天花板分析（上限分析）</p>
<h3 id="章节20-总结与感谢"><a href="#章节20-总结与感谢" class="headerlink" title="章节20 总结与感谢"></a>章节20 总结与感谢</h3><ol>
<li>监督学习<br>线性回归，逻辑回归，神经网络，支持向量机SVM</li>
<li>无监督学习<br>K-Means，主成分分析PCA（降维），异常检测算法Anomaly detection</li>
<li>特定应用和话题<br>推荐系统，大规模机器学习（并行，映射）</li>
<li>如何构建机器学习系统<br>偏差/方差，正则化<br>召回率，F1分数 训练集，交叉验证机，测试集<br>学习算法，学习曲线，误差分析，上限分析</li>
</ol>
<h3 id="感谢"><a href="#感谢" class="headerlink" title="感谢"></a>感谢</h3><p>共8天时间初步学习完毕吴恩达教授的机器学习系列视频，受益匪浅，流连忘返。感谢吴教授的辛苦付出与耐心传道，敬佩之情滔滔不绝！</p>

      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-3"><a class="nav-link" href="#章节1-绪论：初识机器学习"><span class="nav-number">1.</span> <span class="nav-text">章节1 绪论：初识机器学习</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节2-单变量线性回归"><span class="nav-number">2.</span> <span class="nav-text">章节2 单变量线性回归</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节3-线性回归回顾"><span class="nav-number">3.</span> <span class="nav-text">章节3 线性回归回顾</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节4-配置"><span class="nav-number">4.</span> <span class="nav-text">章节4 配置</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节5-多变量线性回归"><span class="nav-number">5.</span> <span class="nav-text">章节5 多变量线性回归</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节6-Octave-Matlab-教程"><span class="nav-number">6.</span> <span class="nav-text">章节6 Octave/Matlab 教程</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节7-Logistic回归"><span class="nav-number">7.</span> <span class="nav-text">章节7 Logistic回归</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节8-正则化"><span class="nav-number">8.</span> <span class="nav-text">章节8 正则化</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节9-神经网络学习"><span class="nav-number">9.</span> <span class="nav-text">章节9 神经网络学习</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节10-神经网络学参数的反向传播算法"><span class="nav-number">10.</span> <span class="nav-text">章节10 神经网络学参数的反向传播算法</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节11-应用机器学习的建议"><span class="nav-number">11.</span> <span class="nav-text">章节11 应用机器学习的建议</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节12-机器学习系统设计"><span class="nav-number">12.</span> <span class="nav-text">章节12 机器学习系统设计</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节13-支持向量机"><span class="nav-number">13.</span> <span class="nav-text">章节13 支持向量机</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节14-无监督学习"><span class="nav-number">14.</span> <span class="nav-text">章节14 无监督学习</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节15-降维"><span class="nav-number">15.</span> <span class="nav-text">章节15 降维</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节16-异常检测"><span class="nav-number">16.</span> <span class="nav-text">章节16 异常检测</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节17-推荐系统"><span class="nav-number">17.</span> <span class="nav-text">章节17 推荐系统</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节18-大规模机器学习"><span class="nav-number">18.</span> <span class="nav-text">章节18 大规模机器学习</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节19-应用举例：照片OCR（照片光学字符识别）"><span class="nav-number">19.</span> <span class="nav-text">章节19 应用举例：照片OCR（照片光学字符识别）</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#章节20-总结与感谢"><span class="nav-number">20.</span> <span class="nav-text">章节20 总结与感谢</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#感谢"><span class="nav-number">21.</span> <span class="nav-text">感谢</span></a></li></ol></div>
            

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